Automated warehousing using robotic forklifts or other material handling vehicles

ABSTRACT

Automated inventory management and material (or container) handling removes the requirement to operate fully automatically or all-manual using conventional task dedicated vertical storage and retrieval (S&amp;R) machines. Inventory requests Automated vehicles plan their own movements to execute missions over a container yard, warehouse aisles or roadways, sharing this space with manually driven trucks. Automated units drive to planned speed limits, manage their loads (stability control), stop, go, and merge at intersections according human driving rules, use on-board sensors to identify static and dynamic obstacles, and human traffic, and either avoid them or stop until potential collision risk is removed. They identify, localize, and either pick-up loads (pallets, container, etc.) or drop them at the correctly demined locations. Systems without full automation can also implement partially automated operations (for instance load pick-up and drop), and can assure inherently safe manually operated vehicles (i.e., trucks that do not allow collisions).

REFERENCE TO RELATED APPLICATIONS

This Application is a continuation of U.S. patent application Ser. No.15/715,683, filed Sep. 26, 2017, now U.S. Pat. No. 10,346,797, whichclaims priority to U.S. Provisional Patent Application Ser. No.62/399,786, filed Sep. 26, 2016, the entire content of both applicationsbeing incorporated herein by reference.

FIELD OF THE INVENTION

This invention relates in general to containerized material handlingsystems, and more particularly, to a method and system for locating,identifying, acquiring, stacking, and transporting inventory itemswithin an inventory system.

BACKGROUND OF THE INVENTION

Modern inventory systems, such as those in container yards, warehouses,superstores, mail-order, e-commerce warehouses and larger manufacturingfacilities, use dedicated crane and gantry storage. Such environmentsalso utilize retrieval systems that require dedicated space and largecapital investment or vertical pallet or container stacking served bymanually driven fork or container hauling trucks. The former providesfast, accurate responses to requests for load, unloading and inventoryof items in transit but is a large integrated capital and spaceinvestment. On the other hand, manually driven container vertical lifttruck based storage systems or yards can cause delays and backlogs inthe process of responding to load, unload and inventory requests, andfurthermore require drivers and loaders and their salaries, benefits,and management burden.

Historically, most loading, unloading and inventory systems are basedaround vertical storage stacking using pallets or other standardizedcontainers. This arrangement offers a compromise between easy access andthree-dimensional storage to reduce area footprint. This type of storageis accessed by forklifts and container handlers of variousconfigurations. The typical way this type of three-dimensional storageis automated is through gantry style S&R or crane units that providethree-dimensional access to the pallet storage locations along a fixedset of travels (i.e., row selection, perhaps through an automatedconveyor; column selection with a pallet transport device moving along apreplaced rail, and vertical pallet/container retrieval or storage alongan elevator lift mechanism).

The type of automation just described is expensive, requires rework ofthe entire storage/retrieval space, and is an all or nothingproposition—one cannot practically partially automate a space—it eitheris automated or remains manually retrieved with forklifts or containerhandlers.

As used herein, “container handler” or “material handling vehicle”should be taken to include any type of manually operated,semi-automated, or fully autonomous truck, platform or unit associatedwith material transport, with forklifts being one example. “Material”should be taken to include containers, boxes, pallets, loads, and soforth, which are transported from one location to another by a materialhandling vehicle. The invention disclosed in our prior U.S. Pat. No.8,965,561, Jacobus et al. describes an approach that assumes use ofconventional forklifts and similar container transport platforms, someof which may be equipped with automation enabling them to operate safelyalong with manually drive lifts into and out of the warehouse. The '561Patent discloses manual systems enhanced with automation to support moreproductive automated load acquisition and placement, as well as toimprove operator safety by cueing proximal obstacles to the operator inreal time.

As shown in FIG. 4A (FIG. 8 of the '561 Patent, incorporated herein byreference in Para. [0015]), a vehicle automation controller (46)receives a mission plan (47) over a local communications link (48). Thecontroller (46) interfaces with various on-board modules, includingsubsystems for auto-location (61), obstacle detection (76),pallet/payload identification (49) and drive-by-wire (57). The locationsubsystem (61) interfaces to various sensors, including absolute andrelative location. Absolute sensors include cameras, lasers/LIDAR andother location sensors (58), magnetometers (80), and GPS (79), whereasrelative sensors include wheel encoders (77) and inertial measurement(IMU). As used herein, “controller” should be taken to include computer,processor, CPU or other hardware and software used for automation,including location computation, as well as the processing images fromcameras and inputs from sensors.

The '561 Patent, further describes pallet engagement and disengagementas beginning with inventory requests; i.e., requests to place palletizedmaterial into storage at a specified lot location or requests toretrieve palletized material from a specified lot. These requests areresolved into missions for autonomous fork trucks, equivalent mobileplatforms, or manual fork truck drivers (and their equipment) that areautonomously or manually executed to effect the request. Automatedtrucks plan their own movements to execute the mission over thewarehouse aisles or roadways, sharing this space with manually driventrucks. Automated units drive to planned speed limits, manage theirloads (stability control), stop, go, and merge at intersectionsaccording human driving rules. The automated units also use on-boardsensors to identify static and dynamic obstacles and people and eitheravoid them or stop until potential collision risk is removed. Safetyenhance manual trucks use the same sensors to provide the operator withcollision alerts and optionally automate load localization andacquisition behaviors for productivity enhancement.

Automated or partially automated trucks have the ability to execute taskspecific behaviors as they progress along the mission, includingvisiting palletizing/depalletizing, refueling, based upon locations inthe order defined by the mission. The vehicles have the ability to driveinto or through tight openings using sensor-based driving guidancealgorithms, and perform pallet finding and engagement and disengagementonto pallet stacks and into shelving units. Load control automatedtrucks also can identify pallet locations and pallet identificationcodes by standard commercial means including but not limited to RFID,barcode, UID, optical characters on printed labels through computervision/camera, and RF code readers. Each automated truck can be placedinto manual drive mode at anytime, and along with unmodified manual forktrucks can be driven over the shared space concurrently used byautomated trucks. The automated lift can sense pallets or load positionsto control the loading and unloading (or stacking/unstacking) operation,can read pallet identifications (barcodes, RFID, printed labels), canidentify moving and fixed obstacles, and can plan from-to paths throughusing data in the warehouse map.

Manually driven trucks can also be enhanced to include obstacleproximity sensing systems that, instead of directly changing the truckpath behavior, alert the operator as to presence or unsafe proximity tothe obstacle, indicating a collision risk and even partial automationthat might change the trucks speed or cause it to initiate automatedbraking. Similarly, partial or complete automation of the loadacquisition or placement function of a manual truck can be included thatoperate as indicated for fully automated trucks in the previousparagraph to support more automated and efficient load pick-up andplacement operations.

The actual load pick-up and placement operations (palletizing anddepalletizing) are described as utilizing two front mounted lasersensors are shown in FIG. 1 (FIG. 6 in 8,965,561). One laser sensor (34)is mounted on the mast near the floor. This unit can be scanned up anddown at a small angle by tilting the mast forward and reverse (35). Thissensor is primarily tasked with detecting forward driving obstacles, butcan also be used to find pallets and pallet stacks. A second lasersensor (36) is fixed to the fork elevator and sweeps a half cylindricalarea (37) as the lift moves up and down the mast. This sensor is taskedprimarily for detecting pallet fork openings and the top pallet in apallet stack, but it too can aid system in sensing obstacles whendriving forward. It is noted that between these two sensors theautomated truck has almost 100% unobstructed obstacle visibility and isthus, far superior to a human driver's visibility because he/she has tosight through the front forks.

FIG. 2 shows placement of several video based sensors used to find andread a pallet identifier. The top camera (39) images the entire side ofthe pallet (40) and uses image processing to identify the grossplacement of the bar code, UID, Character notations (OCR) or otheridentification markers. Then the two smaller field cameras (41) are usedto hone in and read the code identifier at sufficient resolution toachieve reliable code deciphering. Alternatively, or in addition, thesesensors could include a short range RFID reader or any alternativepallet identification method. An advantage of using cameras is that theycan also be used to place roadway or path segment signage up in thewarehouse.

FIG. 3 shows a two-dimensional type of barcode (42) that is in thepublic domain [Artool Kit, 2012] that can code both content data (theinternal bit pattern codes the content) and location of the lift truckrelative to the sign placement location. For this code a camera (43) andsimple image processing algorithm finds the black square frame,resolving the location of its corners. It then reads the internal bitpattern adjusted for perspective skew. If the deciphered code is“correct”, i.e. can be validated as having been assigned to a knownlocation, its location when added to the offset from the code to thelift truck provides the truck automation controller an exact fix withinthe warehouse area. These codes could also be placed on the floor aseasily as anywhere other known location in the warehouse.

In FIG. 4, summarizing automated truck functions, pallet engagement (73)and disengagement (74) as described use range data sets, but in anengagement task specific manner. Pallet engagement is performed in threesteps. First the lift truck approaches the approximate position of thepallet stack provided to it as the source or destination location (withorientation) defined by the system controller and provided to the truckin the mission definition. In the second step the truck stops andevaluates its local obstacle map (FIG. 10) for the nearest pallet stackat or beyond its stop position, which it then aligns to. In the thirdstep, the truck uses its mast-controls-by-wire (75) and sensors to:

-   -   1. Scan and capture the pallet stack from bottom to top (or to a        predefined maximum height)    -   2. Determine the precision position of the pallet stack face    -   3. Move the forks to the correct position for stack approachment        (for picking a pallet this is alignment with the fork holes of        the topmost pallet; for placing a pallet this is alignment to        the space just above the topmost pallet)    -   4. If a pick operation, it optionally uses the wide area camera        (39) to find the pallet identification barcode and one of the        narrow angle cameras (41) to identify the pallet by reading its        pallet identifier. If placing a pallet, lot identification is        done on any pallet that is down from the topmost pallet in the        stack. Alternative means of identification can be use with or        instead of these methods, including RFID, etc.    -   5. Upon pallet or lot identification, the lift moves forward to        align the payload pallet to the stack (or to insert the forks        into the topmost pallet holes)    -   6. To engage, the lift truck lifts the topmost pallet up and        pulls back away from the stack; to disengage, it lowers the        forks until the pallet rests on the top of the stack and then        the lift slightly lifts up and pulls back away from the stack.    -   7. It then lowers it forks to the predetermined safe travel        level above the floor, plans its next movement, and executes the        plan, to take the lift and its payload to the next destination        (or back to the source location).

SUMMARY OF THE INVENTION

This invention relates in general to containerized material handlingsystems, and more particularly, to a method and system for locating,identifying, acquiring, stacking, and transporting inventory itemswithin an inventory system. This disclosure describes how pallets areengaged and disengaged, both mechanically and in conjunction with videoand 3D range measurement sensors. Aspects regarding the fusion ofsensing, mechanical action, and motion planning to accomplish materialacquisition, handling, and dispersal are described in the backgroundsection of this disclosure and in U.S. Pat. No. 8,965,561, the entirecontent of which is incorporated herein by reference.

In accordance with this invention, an automated material handler cansense pallet/container load positions, read load identifications(barcodes, RFID, printed labels), identify moving and fixed obstacles,and plan from-to paths using data in a warehouse or yard map. Based onsensing behavior, the automation kit can obey human-like driving rules(for instance, maintain spacing with the moving object in front, stop atintersections, use the right-first rule when multiple vehicles arrive atthe intersection at the same time, drive around stopped obstaclessafely, stop for pedestrians who cross the travel area intersecting thelift's travel route, etc.). Thus, they navigate to approximately where aload will be delivered or acquired.

Navigation out-of-doors may be accomplished by precision GPS fused withorientation and inertial sensing as previous described in U.S. Pat. No.8,965,561 and commonly employed by autonomous or driverless vehiclesdescribed as least since 1985 experiments demonstrated on the DARPAAutonomous Land Vehicle and Carnegie-Mellon Navlab. However, indoornavigation by GPS does not work because building structure blocks theradio reference information used for it. Alternatives include:

-   -   Embedded floor wires that are tracked by magnetic or other        electronic means marking drive paths. The automated vehicle can        then maneuver along a path by staying in position relative to        the wire. At lane changes or intersections, the vehicle can go        off wire for a short time using Dead Reckoning with wheel        encoders and/or inertial sensors until a new wire track is        reacquired.    -   Painted lines on the floor effectively track by optical or video        sensors, also marking drive paths or lanes. The automated        vehicle can then maneuver along a path and change lanes or turn        at intersections in the same way as with embedded wire tracks.    -   Retro-reflective targets mounted on walls with line-of-sight to        a laser beacon on the vehicle. By measuring the angle to two        reflectors, the automated vehicle can know its location and with        an addition sighting (or an alternative orientation sensor) it        can know its heading as well. Thus, paths, intersections, etc.        can be defined on a virtual map the vehicle can track.    -   Magnetic Markers in the floor. These operate like discrete        breadcrumbs marking points on the drive path between segments        driven by inertial Dead Reckoning.

Each of these approaches, previously described in the literature, havestrengths and weaknesses. Embedded wires or magnetic markers are costlyto install, allow only limited motion paths, but are rugged and robustto wear and tear. Painted paths are low cost, but wear off or get dirtyover time and require maintenance—while also only allowing limitedmotion paths. Wall retro reflectors require less maintenance orcleaning, but require that there be lines of sight from vehicle to atleast two reflectors for any absolute position update. Furthermore, thework space floor has to be maintained to planar to a high precision.

Finally, these means of navigation only get a vehicle close to its finalpallet pick-up or put-down location. Over time, due to limitedautonomous system precision or imprecision from also employing manuallydriven material handling trucks, lot locations (one or more pallets withsimilar parts stored or stacked on them) are variable within sometolerance or epsilon of the location know in an inventory managementssystem delivering requisition commands to the autonomous systems. Formanual systems, an operator recognizes and compensates for these smallpositional errors at the terminal point, and therefore autonomouslycontrolled material handlers have to have similar capabilities.

In this invention disclosure we describe using a combination of videocapture and processing, 3D range measurement and processing, anddefinition, imaging, and capture of a location estimating barcodes toallow automated material handling trucks (or manually controlled truckswith automated material acquisition or placement functions to relievethe drive of complex motion control operations) precision locate,capture, and manipulate pallets or container for loading, unloading andstacking/destacking operations.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates Lift Truck Collision Detection Sensors;

FIG. 2 depicts Lift Truck Pallet/Lot Identification Sensors;

FIG. 3 shows how Artool Kit barcodes localize the camera (affixed to thelift) offsets to the code location;

FIG. 4A is a block diagram of a material handling system according tothe invention;

FIG. 4B illustrates Automates Lift Control Software Architecture;

FIG. 5 shows Path Following;

FIG. 6 depicts Multipath in Ultrasonic or RF locators operating in awarehouse;

FIG. 7 shows Absolute error character as compared to Dead Reckonederror;

FIG. 8 illustrates how rows and columns create roads;

FIG. 9A shows how truck trailer beds or loading platforms provide edgesthat can define paths;

FIG. 9B depicts position and orientation from two or more fixes;

FIG. 10 shows how laser beacon based fixes require a flat floor so thatnarrow laser beam can hit the retro reflective target at long distances;

FIG. 11 illustrates doorway finding;

FIG. 12 shows doorway defined in an absolute coordinate system;

FIG. 13 depicts the way in which barcodes localize the camera (affixedto the lift) based on code location;

FIG. 14 depicts target barcodes surrounded by light to dark boarders;

FIG. 15 shows location fusion based on localization features, GPS, andDead Reckoning;

FIG. 16 illustrates how codes mark the ends of aisles or intersections;

FIG. 17 shows small codes for uses like finding pallet location, palletIDs, pallet fork holes;

FIG. 18 depicts orienting codes to span depth improves range, pitchangle, and yaw angle accuracy;

FIG. 19 shows targets with raised features to enhance range, pitch andyaw accuracy;

FIG. 20A illustrates a barcode on pallets;

FIG. 20B illustrates barcodes on shelving or storage locations;

FIG. 21 depicts endpoint navigation error;

FIG. 22 shows top pallet engagement and removal;

FIG. 23 illustrates pallet placement and disengagement;

FIG. 24 shows multiple pick-up to place pallet in proper loadedposition;

FIG. 25 illustrates barcodes to localize specific locations on a palletor container;

FIG. 26 illustrates barcodes to localize the entire pallet or container;

FIG. 27 illustrates barcodes to identify container or pallet contents ortype;

FIG. 28 depicts the placement of one pallet or container down pin-by-pinwith pushes in between placements to align to the next pin to beengaged;

FIG. 29 shows a Truck Trailer Signature;

FIG. 30 shows an ISO Container Signature;

FIG. 31 depicts finding Labels or Barcodes;

FIG. 32A illustrates a code viewed head-on; and

FIG. 32B illustrates the code of FIG. 32A as elongated when viewed witha highly slanted code of 45 degrees.

DETAILED DESCRIPTION OF THE INVENTION

This invention resides in an automated material handling, transportingsupporting inventory management system that removes the requirement tooperate fully manually using conventional vertical storage and retrievalmachines and or cranes. The approach assumes use of conventionalforklifts and similar container transport platforms, some equipped withautomation sometimes operating safely along with manually drive liftsinto and out of the warehouse. In addition, some manual systems may beenhanced with automation to support more productive automated loadacquisition and placement as well as to improve operator safety bycueing proximal obstacles to the operator in real time, as disclosed inthe '561 Patent referenced herein.

As further described in the '561 Patent, driverless vehicle navigationGPS and inertial dead reckoning provides an effective technique forautomated truck self-location (and therefore provides a convenient meansfor controlling the truck to a predefined path) when operating outdoorswithin line of sight of GPS satellites. However, indoors where line ofsight is denied, other means must be utilized. While floor markers ofvarious types have been used in the automated guided vehicle industryfor many years, they suffer from the need to preplace path or fiducialmarkers on or in the floor and limit vehicle drive path options [U.S.Pat. No. 7,826,919, D'Andrea, et al.].

Our approach uses specialized and generalized:

-   -   Row or edge following (for instance identification of aisles as        floor areas extending between vertical features, typically walls        or shelving units);    -   Door finding and tracking (identifying openings in walls or        shelving units);    -   Target pick/placement locations by identifying and locating        templates or models (matching a object, example: truck trailer,        shelving unit, or other location using a 3D model of its shape        as viewed from a material handling truck from its approach track        to that object);    -   Pallet opening, edges, and top locations (identification of two        openings low down for fork engagement; locating edges to the        left and right of the pallet front to identify extent, and        locating pallet or pallet stack top to identify where the top        pallet in a stack is either for removing it or stacking another        pallet on top of it);    -   Identifying pallet locations, pallet types, lot identifiers, and        key isle-way locations by barcode and/or printed label        identifiers linked to those locations on a digital map of the        traversal space.

Indoor Navigation to Load or Unload Points

Maps, lanes, free maneuver areas—The core capability of autonomousdriverless vehicles is self-location knowledge. By fusing inputs fromsensors, wheel revolution counters, and inertial units, theself-location subsystem continuously estimates material handling trucklocation within the maneuver area. For driverless cars that operate outof doors, this has been described as a fusion of GPS geolocation signaland inertial sensors that measure all locations by extrapolation offorces and torques applied since the last reliable location fix. Theinertial component provides for both filtering orientation and positiondetermined by GPS through the assumption that small location variationsare not actual location changes if the vehicle has not been commanded tomove in all direction variation might be reported, and can providepositional estimates based on integration force and torques intoposition and orientation changes of vehicle location extrapolated fromthe last reliable GPS fix.

Paths along map encoded routes, or across areas where free driving isallowed, can be enforced by comparing the vehicle self-locations againstplanned paths, error or path deviations, thereby generating steeringcorrection feedback so that the vehicle approximately follows theplanned path to accomplish movement from a point A to point B alongallowed driving lanes. Even maneuvers to circumnavigate around obstacleswill be generated and executed as small localized path changes andtherefore are accomplished through the same basic steering and movementcontrol system [FIG. 5]

When the automated vehicle operates to follow paths to endpoints insideof a warehouse, using GPS as the reliable position fixing sensor becomesimpossible because line of sight reception of GPS signals fromsatellites is not possible. While some systems based on RF radiolocation or ultrasonic location finding have been suggested for indoorpremises, all broadcast approaches suffer from inaccuracies due tomultipath (i.e. more than one possible path to the broadcasting beaconand back again to a receiver) prevalent in dense warehouse typesettings. For instance, shelving units might be made of metal or mayhave metal supports, each of which becomes a disbursing and reflectingelement to radio frequency signals. Each reflection generates a newtransmit or return path of length different to straight line of sightpath from target to receiver, and thus confounds computations thatconvert sightings to vehicle localization [FIG. 6].

Our approach for indoor navigation is therefore based on (a) trackingvehicle movement across the warehouse floor as precisely as possible(Dead Reckoning) and (b) identifying, sighting, and tracking features atknown locations within the facility (location fixed features or “LFFs”),either naturally occurring or placed, and using that information tolocalize the automated vehicle from key point to the next point (andusing Dead Reckoning for localization between where these feature pointsare precisely known). Naturally occurring features are already presentin an environment, and include pre-existing structures such as floors,walls, doorways, shelving units, and so forth, whereas placed featuresinclude fiduicials, computer-readable codes, etc., which areintentionally added to the naturally occurring, pre-existingenvironment.

Dead Reckoning—This is localizing the vehicle position from preciselymeasuring movement. If a vehicle moves from a known point A [x, y] andorientation β by a known distance and orientation change [Δx, Δy, Δβ],then we can know its position from the known point as [x+Δx, y+Δy,β+Δβ], indefinitely as the Δ measurements are perfectly accurate. Due tomeasurement error, €, they will not be, so practically error creeps intothe position estimates proportional to the number of measurements taken,n*€, and eventually makes this method of localization too imprecise touse. For shorter intervals between more accurate localizations from GPSfixes or sightings of facility location fixed features (LFFs), DeadReckoning is very useful since the vehicle continuously knows itsapproximate location for continuous path following (with steeringcorrections).

Absolute coordinate fixes like those from GPS and LFFs are collectedwith non-systematic errors (typically random walk errors within somespecified circular error probability or CEP). Therefore, vehiclelocation based on coordinate fixes will vary within some predictablecircular error probability radius, making it look like the vehiclespontaneously “teleports” small distances around where it really is atany given time. On the other hand, Dead Reckoned estimates may generateestimates with errors that are unbounded over time. These estimates areonly erroneous by smaller multiples of € of shorter intervals, i.e. isonly erroneous by n*€ which over shorter time frames is <<CEP [FIG. 7}.Thus, if we estimate location based on a smoothed weighted sum of theabsolute coordinate fix-based location and the Dead Reckoned location,w₀*[x₀+ΣΔx, y₀+ΣΔy, β₀+ΣΔβ]+w₁*[x₁, x₁, x₁], and feed this back to thesystem as its best overall location solution, the Dead Reckonedcomponent “filters” the absolute component's CEP error while theabsolute component bounds the worst case Dead Reckoned error (i.e. keepsn*€<CEP).

Note that while this is described for movement over a two-dimensionalsurface, the approach readily generalizes to movement over three or moredimensions. Kalman filters combining both Absolute and Dead Reckonedlocalizations present one means for implementing the aforementionedapproach, but other means that include position curve or line fitting,fuzzy logic, or combinations including probabilistically estimatedweights are alternative implementations. Also note that direct positionsensor like encoders measuring joint rotation or laser range finders ofcameras measuring distance to walls, floors, ceilings, or specificobjects are other forms of Absolute sensing as is GPS measurement madeout of doors.

The preferred embodiment measures the Dead Reckoning Δs for materialhanding trucks by:

-   -   Measuring wheel revolutions (or alternatively drive shaft        revolutions) for one or more of the wheels that make the vehicle        move    -   Measuring forces acting on the truck due to steering and        acceleration/deceleration through force sensing inertial sensors        (a position estimate in [x, y, z] can be estimated as        [Σ(ΣFΔx+v_(x0))+x₀, ΣΣΔFy+v_(y0))+y₀, ΣΣΔFz+v_(z0))+z₀], where        [v_(x0),v_(y0), v_(z0)] are initial velocities in x, y, and z,        and [x₀, y₀, z₀] are initial positions).    -   Measuring angle changes due to truck steering and steering rate        through angular inertial sensors (an orientation estimate        [α,β,γ] can be estimated as [ΣFΔα+α₀, ΣFΔβ+β₀, ΣFΔy+γ₀], where        [α₀, β₀, γ₀] are initial orientation angles.    -   Measuring orientation angle [α,β,γ] as estimates deviation from        North magnetic vector (i.e. through a magnetometer).        Floor Wires, Lines, or Magnetic Markers

Floor embedded wires and painted lines are readily detected byinexpensive inductive or optical array sensors and have been used forAGV navigation for many years. Where the marker is detected relative tosensor midpoint generates a steering error signal to keep the vehicle ontrack. To move from drive track to drive track or during maneuversthrough lane track crossings (intersections) drive segments of open loopDead Reckoning must be performed. Spacing to the next lane line markershould be small enough that accumulated Dead Reckoning error is lessthan the lock-in interval of the line tracking sensor. Magnetic markerswork in a similar way. When a magnetic field detector array (forinstance Hall effect sensor) is passed over the marker a steering erroris generated by the position of the marker under the detector array.Since markers are placed at intervals, Dead Reckoning from one detectedmarker to the next must be accurate enough to detect the next markerwithin the detection array interval.

Row or Edge Following

Autonomous trucks typically include forward, side-looking, and rearlooking obstacle detection sensors, such as optical laser radars (LADARor LIDAR), acoustic proximity sensors, or stereo range detecting visionsystems (refer back to FIG. 1). These systems provide the driverlessvehicle with direct sensing of how close it is to any potentialcollision. This data is used during driving to either trigger a stop andpath replanning action or a path modification so as to steer around thepotential obstacle collision. However, these systems also provide ameans for detection of navigation relevant information as well. In awarehouse or container yard area [FIG. 8], the space is typicallydivided into rows or shelves of containers and free driving lanes thatare placed between the rows or between a row and the wall, making of arectilinear set of “roadways.” As a truck drives along one of theseroads, it is possible to detect the lane edges as vertical obstaclefeatures organized roughly as walls to the right and left of the truck.Lane keeping in this type of environment can be achieved by directingthe truck navigational system to “hug” either a left wall or a rightwall maintaining a predetermined distance from that wall. As is the casefor line or wire tracking discussed previous, the truck willperiodically have to use another navigational mode at intersections oflanes, for instance, Dead Reckoning or alternative described below. Notethat navigation along a loading or unloading platform of trailer can beaccomplished in the same way [FIG. 9A].

Retroreflective Targets Tracking

Units are available that rotate a laser beacon at a constant RPM shiningthe laser at even long distances against walls and vertical featuresextending up from the floor of the area of operation. When such a laserilluminates a retro-reflective patch (for instance, a retro reflector,reflective tape, or a corner cube) a sensitive optical detector istriggered in the unit and the angular position of the beam (0 to 360degrees) is recorded as sighting fix [FIG. 9B]. Two such fixes and aknown vehicle orientation (which might be determined by knowing theangle of the lane being travelled, by an orientation sensor such asmagnetometer, or from a Dead Reckoned orientation estimate), or threefixes provide the means for detecting vehicle position and orientationwithin an operating area. Two fixes can provide a location but not anorientation, but several position fixes in a row or three fixes at asingle point allow the orientation to be determined as well. Some AGVsystems that do not employ wire or painted line tracking have employedthis localization approach. The difficulties of this technique aretwofold. First, one requires at least two line-of-sight fixes tolocalize and warehouse areas typically have many floor to ceiling stacksof material, column supports, or shelving units that block lines ofsight (refer back to FIG. 8). Secondly, for the laser to hit a smalltarget at long range it is necessary for the laser/detector to be veryprecisely level and aligned vertically with each target [FIG. 10].Practically this puts a significant floor flatness criterion over theentire operating area, which might be constraining in work spaces withramps and multiple levels.

Door Finding and Tracking

Many applications require a truck to move from one room or area intoanother through a wall opening, or a doorway. Doorways [FIG. 11] arecharacterized by (a) being visible at some distance along a drive trackor lane at an approximately know predetermined location; (b) showing arange discontinuity (i.e. range to objects, obstacles, walls seenthrough the doorway are notably further way than the ranges to walls oneither side of the doorway); (c) having an approximate vertical (v) andhorizontal (h) width that is correct for the doorway (i.e. since we knowapproximately where we and therefore approximately what doorway we arelooking at, the height or vertical clearance and the width should alsoapproximately match the expected sizes for that doorway); (d) having anexpected wall thickness, t, (i.e. as we move close to and through thedoorway, we can track on the door jam, left or right or both, tomaintain truck lane centering and for narrow doorways to avoid hittingon either side). When the doorway is reliably detected it allows thenavigation system to locate truck position relative to the doorway FIG.12 using the coordinate system defined by the vector, D, across thedoorway opening and the vector, P, perpendicular to the doorway vectorin the direction of the truck. Furthermore, since doorways so detectedare fixed objects in the drive space, the doorway origin of a coordinatesystem defined by D and P can be used to provide an indoor absoluteposition fix to remove accumulated Dead Reckoning position estimationerrors.

Barcodes, Reflectors, Lights to Mark Location within Code VisibilityOperating Range

Specialized location detecting bar codes have been used in augmentedreality and some robotic applications for many years. Like all barcodes,these code are detected by algorithms the hypothesize presence of acode, attempt to read the code, and determine that the code was detectedand read properly due to validation checksums present in correct codes,but not properly present in falsely detected codes. Barcodes forlocalization [FIG. 13] then identify geometrical properties of the coderectangle (the four corners) to determine the location in 3D space ofthe code relative to the reading sensor (or alternatively if theposition of the code is known, the location of the reading sensorrelative to that code position information). Localization codes aretypically two-dimensional codes like ARTOOL Kit, ArTag, Goblin Tags, QRcodes, Military Universal ID codes (UIDs), Microsoft “tag” codes,Maxicode, or DataMatrix so they are rectangles that have identifiablecorners.

Such codes also typically have quick target hypothesis features. Forinstance, most of the AR or augmented reality codes are black boxessurrounded by lighter regions (ideally a larger white rectangular box)so that the code recognizer can hypothesize a likely target location byfinding the light dark transitions around the code [FIG. 14]. Some codeslike QR, DataMatrix, or Maxicode use embedded simplified features (QRcode uses three black-white-black target features in three of the fourcorners; DataMatrix uses a black edge on the left and lower side and ablack cross through the center of the code; Maxicode uses a centeredwhite-black-white-black-white-black-white bull's-eye). The algorithmlooks for the code location hypothesis feature, then finds the codecorners and interior region which contains binary encoded data, and thenprocesses the interior data to acquire the data encoded by the barcode.This decoded data is then validated through checksum(s) or by look upinto 1a legal codes list to determine if the code was properly read. Ifvalidated, the four corners of the code are used to determine thelocation of the code or the camera reading the code.

This provides a very flexible means for simplifying recognition of knownlocations for the automated robotic unit. Any location identifying codecan be placed in the environment at a predetermined location. When atruck mounted camera sees the code at close enough range, it canautomatically identify and decode the code and knows where the truck isrelative to the code (Camera is located by the code image and where thiscode is known to be located in the environment, and then since thecamera is mounted at a known location relative to the truck, the trucklocation becomes known in the environment). If multiple codes aredetected in one or more cameras the truck location can be moreaccurately known through least squares estimation. Because the codes arevisually unique in the environment, a single powerful recognitionalgorithm can detect them, by-passing the obvious alternative ofrecognizing a large variety of different visual features of the naturalenvironment. On the other had mixing natural location features (such asdoorways discussed previously) and placed features based on codes iseasily accommodated through the truck localization data fusion processdescribed previously for mixing GPS and Dead Reckoning, replacing GPS bylocation estimates derived from placed codes or natural features likethe doorway [FIG. 15].

In our use of the codes, we suggest placing them at the end of travellanes [FIG. 16], perhaps across intersections of at the corners ofshelving units so that as a truck moves about the warehouse it seescodes at 5 to 50 meter intervals. If a code has to be visible from onlya short distance, it might be made small so that it becomes resolvableonly when the truck is close to an endpoint [FIG. 17]. On the other handif a code will used to navigate along a long track it can bemanufactured larger so it is resolvable from a greater range. The basicaccuracy of the codes is directly proportional to the solid angle thatit projects into the automated truck's camera pointed in roughly thedirection of the code. Furthermore, the accuracy of localization basedon the code depends on its orientation to the truck. Generally, moreaccuracy in angular resolution [α,β,γ] is achieved if the code is notoriented perpendicular to the vector from the truck sensor to the code[FIG. 18]. Tilting code up to 45 degrees to create target depth relativeto the detecting sensor improves this angular resolution markedly. Notethat some codes like the docking target that has been employed forrobotic grasping of satellites in space by NASA provide this depthenhance angular resolutions by actually elevating part of the targetcode as shown in FIG. 19.

Each code detection provides the truck navigational system with anaccurate driving direction relative to the target (steering angle tomaintain a pitch yaw and roll fixed to the target center) and as thetruck gets closer the corners are more accurately resolved yielding afull [x, y, z, α,β,γ] fix that can remove accumulated Dead Reckoningposition errors. Thus, visual target fixes can provide indoor absolutelocalizations, substituting or augmenting GPS fixes that only operateaccurately outdoors. Because the targets can be detected along aninterval in both horizontal and vertical, they are superior toretro-reflective targets discussed previous because they tolerate betternon-uniform floor angles and elevations, and allow for less precisepositioning of the target themselves for easier making field deployment.

Floor or Ceiling Barcodes

Locating barcodes do not have to be placed as previously discussed onwalls or at intersections like street signs. They can also be placed onthe floor or the ceiling as easily. They are detected in exactly thesame way and can be mapped to known locations through look up into alocation data list based on the uniqueness of the interior content ofthe code. Ideally they should be placed at location not likely to beworn away by repeated automated truck operations (for instance, notwhere a truck wheel will repeatedly go over them). Codes on floors haveto be maintained to accumulated dust and debris does not obscure themover time.

Row or Edge Following by Side Looking Barcodes

In earlier discussion of row or edge following, we described how a driveline can be maintained by tracking the vertical edge of a containerstack, shelving unit, or wall sensed by collision detection sensors. Ina similar manner, points along a shelving, container stack, or wall canbe marked with 2D or 1D barcodes incidental to warehouse operation (FIG.20). Each payload or pallet [1] might be so marked as its means ofunique identification, or pallet storage locations might be marked ([2]as discussed later) to define payload placement location or lotlocations. These codes meet the criteria for navigation when properlyread, decoded and mapped to location. Each can provide a unique vehicleto shelving unit location that can be tracked as easily as thegeometrically defined shelf, container stack, or wall vertical geometricstructure. Furthermore, even without mapping the code read to a specificlocation, the location of any valid code relative to truck position in adrive lane can be used for lane keeping along a row.

End Point Maneuver to from Loads for Pick-Up or Placement

As referenced earlier, basic autonomous navigation includes someendpoint error that is bounded by the CEP of bounding or absolutesensors like GPS or the various optical, wire of line guided, ormagnetic guidance sensors and build-up of error of dead reckoning fromthe last location estimate acquired by a bounding sensor fusion (FIG.21). In our system this is typically between 2 to 20 cm depending on thebounding sensor CEP and nearness to both a know target or barcodelocation and how good the dead reckoning system can extrapolate fromthat location.

Pallet fork opening for a 48″ pallet are nominally 16″ wide and perhaps4″ high (40 cm wide and 10 cm high) so while it might be possible tonavigate right into a pallet opening within the navigational systemoperating tolerance, there are factors that work against this. The firstis that acquiring the load close to center of each pallet opening ismore favorable so that the load of the pallet on the forks is wellbalanced. The second is that pallet put downs and stacks can possibly beoff at the extremes of the navigational error range (+ or −20 cm of anextent of 40 cm which is nominally the side of the pallet fork openingwidths). Thirdly, in a warehouse where some of the pallets and stacks(alternatively referred to as lots) are placed by human drivers, theymay or may not be place in exacting the right location. And finally,some putdown and pick-up locations may be relative to a target location(for instance, where a flat bed truck has been parked for loading orunloading—at an approximately known place but potentially off by one toseveral meters in any direction).

Therefore a practical autonomous material handling truck must be capableof modifying its endpoint for load engagement and disengagement based oncues that can be measured at the load or unload point. Our approach forendpoint guidance or navigation is therefore based on employing locationfeatures on the load (pallet) and on the point of pick-up or putdown(i.e. on the trailer, storage shelving unit, or pallets already definingthe lot and stacks in the lot). The following approaches are applicableto various steps of the loading and unloading process:

Pallet Opening, Edges, and Top Locations

Using 3D Laser scanners that capture forward looking range fields,pallet locations and fork insertion point are readily detected becausethe front face of the pallet is closer to the sensor than surfacesbehind, to the right or left, or on top of the top most pallet. Forkinsertion points are detected at the bottom of a pallet. Tops are overthe upper most pallet, and to find this might include using the forktruck forks to raise the 3D Laser scanner (or alternative range sensingmeans) up so that the boundary from top most pallet face to empty spaceabove it is detected within the sensor's field of view. Edges to theright or left are detected as empty space beyond the pallet face surfacedetected (Alternatively for closely stacked containers, boundary markingmight be used rather than empty edge spaces).

Picking [FIG. 22] up a pallet is typically done lined up with the centerline of the pallet left to right (½ the distance from the left side tothe right side) and aligned vertically to the fork insertion locationsof the topmost pallet. Thus, the algorithm will be to find theapproximate pallet location left to right and center the truck onmidpoint line, then move forks up until the laser scanner (oralternative range sensing means) sees the empty space over the topmostpallet, and finally move forks down until that topmost pallet's forkinsertion holes are identified and localize. After this properpositioning, the truck (or forks only with a truck with telescopingforks or clamps) moves forward into the insertion holes until the palletis fully engaged. The forks are raised, and the truck reverses to removethe pallet from its stack, and finally lowers the pallet to the safetravel position (approximately 12″ from the ground). Note that to makethis approach work each pallet stack must be separated by a smallspacing (or other side demarcation that can be detected) and generallylots are separated from each other by a larger spacing. For pallets ofcontainers that are in direct abutment, an alternative means foridentifying edges is required. Generalized computer vision for thisapplication being difficult, we use preplaced barcode, printed labelidentifiers or container edge markings.

When placing a pallet [FIG. 23] the placement location is also found by(a) moving to a predetermined location—see prior discussion on how firstpallet locations can be localized, or (b) by lining up with the centerline of the pallet left to right which is already placed (i.e. when thenew pallet is being stacked onto an existing stack). To find thiscenterline follow the same procedure already described. To find thepallet stack top also use the approach previously described. Howeverrather that scanning down to find the topmost pallet's fork insertionpoints, we raise the pallet being carried up so that it clears thetopmost pallet, the truck moves forward into alignment with the stack,moves the forks down until the carried pallet contacts the stack, andthen slight lowers further to remove the pallet weight from each form.It finally reverses to clear the forks from the pallet. Some warehouseshave ceiling height limits so when raising pallet high enough to clearthe top most pallet, we also monitor through a sensor the ceilingclearance. This sensor is likely to be a point laser distancemeasurement device but may be an alternative means like a pre-programmedfixed maximum mast extension height limit (enforce by height measurementin the mast movement assembly) or an alternative optical or otherproximity sensor (video cameras, structured light, magnetic, acoustic).

In both cases above (placement and pick-up) the truck lowers the forksto the safe travel height (typically 12″) and proceeds to navigate tothe next pick-up or placement point respectively.

Refinements of the procedure include: (1) several put-down, forkrepositions, and placement operations to scoot the pallet forward toproper placement or to firmly push the pallet up against the back of thefork carriage; FIG. 24; (2) using barcodes placed on known positions onthe bottoms and or the tops of pallets for determining criticalplacement locations for each pallet to the next in the stack (or frompallet to first placement location)—FIG. 25; (3) using barcodes placedon known pallet locations to determine pallet left, right, up, downlocation FIG. 26; (4) using barcodes to verify that the pallet beinghandled is actually the correct one specified by the robotic trucksmission plan—FIG. 24; (5) using other means (printed labels, RFID, etc.)to verify that the pallet being handled is actually the correct onespecified by the robotic trucks mission plan—also FIG. 27; (6) putting acontainer or pallet down one locating one or several pins at a time,relocating between each pin alignment and placement to allow physicalrealignment so that all of several pins (for instance 4 pins atcontainer corners) become aligned and locked—FIG. 28.

Target Pick/Placement Locations by Identifying and Locating by FormTemplates

The field of target recognition has been of interest to robotics andmilitary for many years. Typically the way such system work is that theform of a location of interest is mathematically encoded as a scale andsometimes rotationally independent feature. One approach is as adimension limited two-dimensional template (a matrix of values thatcapture the object at the designated location and can be matched againsttwo-dimensional sub-matrices extracted from a sensor of beam-formedfocal plane through cross correlation or least squares errorminimization), another is through rotational invariant features(described, for example, in U.S. Pat. No. 6,173,066 to Peurach, et al.,incorporated herein by reference), and third approach is throughcompound feature detectors that look for parts of an object based onrelationships coded in a model or a procedural representation (generallymodel-based computer vision).

We do not generally advocate using these approaches for automatedmaterial handling applications because pallets are so varied thatcreating a small number of templates, rotational invariant features, ormodels to represent them is not possible. On the other hand there areinteresting special cases such as:

-   -   Approaching a truck trailer or a loading platform from the side        or the back—signature of the platform, such as wheels positions,        back end position, trailer or dock flat platform, and empty        space over the platform at some point along the platform length,        can be identified by template or other models [FIG. 29].    -   Identifying doorways—[FIG. 12] Because trucks approximately know        where doorways are located, a signature that shows a surface at        approximately the wall location, then an open space with depth        deeper than the wall location, and then more wall after the open        space can be interpreted as the doorway and truck location        relative to the doorway can be determined from the two door        frame edges A and B (or the coordinate system shown made from        vector D and P). As the door way is approached and traversed,        the door frame thickness and the relative position of the truck        to the door frame can be tracked by also identifying edges G-A        and H-B. For more complex door frames these identification can        be further personalized to capture and utilize in matching these        complexities.    -   Identifying a pallet stack—Signature is a pallet face bounded on        each side by open space as has been described previously.    -   Identifying key parts of pallets or stacks (top, sides, fork        hole at the pallet bottom)—Signature is edges or open hole areas        has been as described previously.    -   Identifying key parts of containers (top, sides, fork hole at        the pallet bottom, or carrying pins on the top four        corners)—Signatures are similar to those for pallets except that        in addition to identifying them from front facing sensor it is        also necessary to have signatures defined for approaching the        contain from its top. From the top sides and top faces are        identified as a generally rectangular top face of a rectangular        prism. For ISO containers, each corner also has a unique        signature for describing the locking pins that are used to lock        the first container to a transport platform of some type (truck        bed, ship deck, etc.) or another container upon which to stack        subsequent containers [FIG. 30].    -   Identifying drive lanes between two parallel sets of pallets        and/or shelving units—The signature is two nominally planar        vertical features (they probably will not actually be planes,        but virtual planes that separate an area on one side where not        driving obstructions can be detected from the area on the other        side where obstructions such as pallet faces or shaving unit        supporting structures are detected) to the left and right        defining a free drive or unobstructed area between.

In each of these cases, the environment is sampled by a sensor (either acamera or a range sensor), sample data is placed into a two-dimensionalarray (either by the sensor or by geometrical re-sampling of the inputdata to form such an array), and the template(s), features, or modelsare applied to the data at a range of locations (and orientations) abouta hypothesized nominal points to determine which location matches theinput sampled data the best. This best location (either a test locationthat minimizes match error or maximizes cross correlation) is thelocation of the item to be picked or placed upon if the match exceeds acritical match value of € (or for least squares is less than a matchvalue of 6). One the location is known relative to the materialtransport truck, robotically controlled movement and articulationmovement can bring the truck load into the correct stacking location orcan acquire a load and pick it up for transport to another location.

Identifying Pallet Locations, Pallet Types, Lot Identifiers, and KeyIsle-Way Locations by Barcode and/or Printed Label Identifiers

Finding, identifying and reading barcoded or label printed informationis a specialization of by identifying and locating by form, where theform is specifically designed to more reliably identified and validated[FIG. 31]. Identification of printed labels or signage is done throughthe following steps:

-   -   (1) Hypothesize (find candidate) labels generally by finding        boxes lighter or darker than the surrounding background in        imagery of approximately the expected size (i.e. sized in x and        y dimension within a specified size range).    -   (2) Within the box find line spacing and character spacing        (usually done by performing light on dark or dark on light        histograms along the y axis (to find likely lines) and the x        axis (to find likely letter spacings per hypothesized lines).    -   (3) Left to right box each character, normalize to standard size        and match (either through multiple template matching or        alternative like pre-trained neural nets).    -   (4) Convert letter matches into character strings, applying any        error correction (for instance spell checking) or heuristic        fill-in algorithms that can be used.

Identifying barcodes is similar:

-   -   (1) Hypothesize (find candidate) labels generally by finding        boxes lighter or darker than the surrounding background in        imagery of approximately the expected size (i.e. sized in x and        y dimension within a specified size range).    -   (2) Within the box apply one or more one-dimensional or        two-dimensional barcode finding algorithms: Usually normalize        the label to front facing standard size followed by the barcode        light to dark line or point finding code with translation into        binary form.    -   (3) Most barcode algorithms include redundant binary information        making it possible to perform either error checking or error        correction.

In both cases above, if the label rectangle is correctly found, and theresolution, contrast, and algorithms operate correctly, a correct codeis generated. If we assume that the code encodes a larger code set thatis required for the mission of identification of products or locationsrequired by the automated material handling system, most of the possiblecodes are unassigned. Therefore additional identification certainty isachieved when the finally decoded code or label is one that has anassigned meaning and/or location to the material handling system. Forthis reason, using codes or labels is preferred to any more generalalgorithm for identifying generalized forms.

Locating the label relative to the automated truck's label readingsensor (often a camera but sometimes a scanned laser reading device), ispossible by knowing the location of the label sides, top/bottom, andtherefore its corners. It is s simple exercise in three geometry andleast squares fit minimization to compute the orientation and positionof the label rectangle [x, y, z, and pitch, roll, yaw] from the camerameasured (5) [x_(low left), y_(lower left)], (6)[x_(upper left),y_(upper left)], (7)[x_(upper right), y_(upper right)], (8)[x_(lower right), y_(lower right)] points. Error in especially range(distance between the camera and the label) is improved if the plane ofthe label is not parallel to the plane of the camera focal plane so thatthe label effectively subtends depth as well as extent vertical andhorizontal.

Thus an object can be reliably identified by assigning it a barcode oran easily decoded label and placing that label at a known locationrelative to the object's larger geometry (for instance at a knownlocation relative to a pallet fork opening [FIG. 17], pallet face [FIG.26], or container aspect [FIG. 25]). Furthermore, by identifyingbarcodes or labels as the automated vehicle progresses by them,therefore where they are relative to the truck, the truck can determinewhere it is with respect to the labeled objects, maintaining or updatingis position estimate as it drives along aisles, to endpoint, palletpick-up, etc. Because each code or label includes redundant informationand item identifying information (either identifying known locations, oritem identifying information), an automated truck can also use theselabels as would a human picker to unambiguously identify each item oritem placement location. Because codes are designed to span very largeproduct identification spaces, practically, any item can be assigned aunique identification code.

Barcodes to Augment Aisle Movement

As described above, items placed on shelving units, items stacked inrows and columns, or shelving units or posts themselves can be labeledor barcoded [FIG. 20]. Therefore, as the automated truck (and camerasfor code reading mounted on it) moves through the storage area, they canreceive position updates derived from correctly decoded and identifiedcodes or labels and localizing based on their known placement locationsin the workspace (while rejecting misreads that map to unassigned codesor incorrectly decoded codes). As with all other means of vehiclelocalization, this information supports accurate lane keeping andlocation of the vehicle between segments of dead reckoning.

Barcodes to Identify Pallet of Pallet Location

Pallet or container retrieval requires approximate positioning of theretrieval material handler through its automated driving behaviors andlocalization means. However, error in this location knowledge andsometimes similar uncertainty about where a pallet or container is placein prior operations (or error in the position for the pallet/containerplace location) makes it necessary to update or correct placementlocation information when the automated truck is located close to itsfinal point of load pick-up or drop.

By placing a localization label or barcode on the pallet or container ata known spot relative [FIG. 26] to is larger geometry, its location iseasily precisely located within a proximal area (or alternative when acode is placed at a known location relative to a load stackinglocations, the stacking location can be precisely known) withoutdetailed knowledge of visual geometry of the load (or placementlocation). Therefore, using codes makes for a generalizable and easy toemploy identifying means for pallet of pallet locations.

Barcodes to Identify Locations and Orientations of Pallets, PalletEngagement Points, and Pallet Placement Points

For certain operations like placing one pallet or container precisely ona stack of containers, the location of features of the containers haveto be precisely known (for instance the locations of the corner hooksused for fastening ISO¹ containers or JMIC² pallets and locking theminto stacks). ¹International Organization for Standards—they define thestandards pertaining to intermodal shipping containers²Joint ModularIntermodal Container

Specifically designed locating labels or codes [FIG. 25] can be used asan alternative to less reliable visual recognition schemes based onidentifying and localized container geometries (through range or cameracomputer vision identification of forms algorithms). The placement ofthese reliably detectable and locatable codes or labels can be made sothat knowing their locations also determined related nearby locations ofcontainer geometrical features by passing more generalized and lessreliable computer vision approaches.

Notes on Practical Reliable Identification of Codes and Labels

-   -   a) Codes are more reliably decoded if they include target boxes        or features around their edges that resemble target bullseyes.        Namely alternative areas or lines of light (white) and dark        (black). [FIG. 14].    -   b) Codes captured by active illuminators (lasers or bright        lights) are less affected ambient lighting and shadowing so are        more likely to be identified correctly in all illumination        conditions.    -   c) Codes that are to be used especially out of doors, have to        tolerate deep shadows that can be cast by container and        automated truck from high sun angles in bright daylight. A very        effective means for creating this tolerance is to employ        compound labels or codes made up of several different atomic        (individual) codes, or codes organized in rows. In this kind of        configuration, localization is possible if only one or several        codes are correctly decoded, so if some codes are rendered        useless due to a deep shadow, missing them does not affect the        reliability to any significant degree. [FIG. 25—two rows of        multiple codes].    -   d) Codes to be used for range or depth estimation should be        positions so as to extend over a depth range. The plane of the        sensor should not be parallel to the code plane. An ideal        arrangement is to place the code rectangle approximately 45        degrees from the plane of the camera focal plane [FIG. 18]. Some        compound codes are purpose build for more reliable depth        determination, for instance with raised features or on pyramidal        structures [FIG. 19]. These arrangement also improve accuracy in        determining of pitch and yaw angle determination.    -   e) Codes likely to be sensed from an extreme angle, can be        extended in the foreshortening axis to improve localization        accuracy [FIG. 32]    -   f) Codes are sized so support the minimum and maximum range over        which they are to be reliably decoded, identified and used for        localization. For instance, a code for localizing a attachment        hook when a pallet is very close to its final proper position        for coupling might be quite small (say between a cm and an inch        on a side). A code used to identify pallets and pallet holes        might be larger to allow identification from several to 10s of        feet (say 4″×4″). Codes designed to be reliably detected,        decoded, and localized for general navigation within rows and        lanes between shelves and containers (i.e. to be reliably seen        out to 50 to 100 feet or more) might be very large (1′×1′ to 1        meter×1 meter). Accuracy of localization is linked back to (a)        how accurate a localization has to be, (b) how many pixels or        scan angle increments have to subtend to code to allow for        reliable decoding).        Linking Locations to Robotic Driving and Pick-Up Drop Operations

The automate material handling truck operates in a manner which isanalogous to most know robotic devices. It is at a location known tosome precision (and its articulations for loading and unloading are atknow locations) measured by internal sensors and its localizationssystems as described.

Each driving or manipulation operation is described by a series ofincremental location changes (or alternatively a string of new nearbylocations in sequence) to which the truck has to reconfigure or moveitself to. The temporal string of new positions are typically generatedfrom a piecewise continuous mathematically described curve whichconnects from the operation starting point to its intended endpoint. Fordriving this is points computed along prescribed paths planned from awarehouse or storage space map. For articulation elements (forks, mastelevation and tilt, etc.) this is computed as movement alongapproximately linear lines connecting starting points in[x_(start),y_(start),z_(start),pitch_(start),roll_(start),yaw_(start)]to ending points[x_(end),y_(end),z_(end),pitch_(end),roll_(end),yaw_(end)].

At each point, the measured position (indirectly measured through anycombination of approaches previously describe and direct positionmeasurement from on-board sensors where that is possible) of the truckand its articulations are compared to the points being generated fromthe motion plan (derived from maps or known endpoint locations forpick-up or drop as well as intermediate points). The difference becomesan error term which provides negative feedback to the truck motioncontrol or servo systems bringing actual measure locations successivelycloser to the plan generate locations.

The key to smooth effective implementation of motion to accomplishmission objectives is (a) to maintain accurate knowledge of the currentlocation through either direct high accuracy-high interval rate measuredata (say from encoders, tilt measuring devices, inertial measurements,etc.) or by indirect fusion of all means for localization of the truckin its drive space and payloads in the warehouse or storage space(through a combination of the means already described or equivalentmeans). Since indirect measurement will inevitably be made periodically(and not continuously relative to the update rates required form motioncontrols) and to variably known precision, it is necessary to used (a)fusion to improve accuracy over individual measurements, and (b) providedirect means (i.e. encoders, tilt measuring devices, inertialmeasurements, etc.) that can be used to smoothly interpolate betweenindirect measurements. Earlier these two complimentary approaches forlocalization were termed dead reckoned or relative positiondeterminations vs. absolute position determinations.

The invention claimed is:
 1. A material handling system adapted for useoutside and within a warehouse or other facility having location fixedfeatures (LFFs), the system comprising: a material transport vehicleincluding a plurality of sensors facilitating indoor and outdoornavigation; wherein the sensors facilitating outdoor navigation includea global positioning satellite (GPS) geolocation sensor; wherein thesensors facilitating indoor navigation include laser or video sensorsoperative to view, identify and track the LFFs; wherein absolutecoordinate fixes are collected using GPS or a combination of GPS and LFFposition to determine the location of the vehicle; and wherein theabsolute coordinate fixes are used in conjunction with dead reckoning isused to determine the location of the vehicle between the LFFs.
 2. Thematerial handling system of claim 1, wherein the LFFs enable the vehicleto pick up, transport and place material to and from different materialstorage areas within the warehouse or other facility.
 3. The materialhandling system of claim 1 wherein vehicle location is based on asmoothed, weighted sum of the absolute coordinate fixes and deadreckoning.
 4. The material handling system of claim 1, wherein the LFFsare preexisting structures or intentionally placed computer-readablecodes.
 5. The material handling system of claim 4, wherein the videovideo or laser sensors are operative to detect and identify thecomputer-readable codes.
 6. The material handling system of claim 5,wherein the computer-readable codes are barcodes.
 7. The materialhandling system of claim 5, wherein the computer-readable codescompound, elongated, or specifically encoded to overcome lightingconditions or view orientation.
 8. The material handling system of claim4, wherein the preexisting structures include floors, isles, walls,doorways, or shelving units within the warehouse or other facility. 9.The material handling system of claim 1, wherein the vehicle is aforklift; and the video or laser sensors enable the forklift to locate,engage, and manipulate pallets for loading, unloading and stacking ordestacking operations.
 10. The material handling system of claim 9,wherein the video or laser sensors are operative to identify one or moreof the following: pallet locations, pallet types, lot identifiers, andkey isle-way locations.
 11. The material handling system of claim 9,wherein the video or laser sensors are operative to identify one or moreof the following: pallet openings, pallet edges and pallet toplocations.
 12. The material handling system of claim 9, wherein thevideo or laser sensors are operative to target pick-and-place locationsby identifying and locating templates or models.